Digital Commons Network^™

Nitrate: The Dr. Jekyll And Mr. Hyde Of Human Health?, Catherine P. Bondonno, Liezhou Zhong, Nicola P. Bondonno, Marc Sim, Lauren C. Blekkenhorst, Alex Liu, Anjana Rajendra, Pratik Pokharel, Dorit W. Erichsen, Oliver Neubauer, Kevin D. Croft, Jonathan M. Hodgson

Research outputs 2022 to 2026

Background: Dietary nitrate has a controversial role in human health. For over half a century, the nitrate content of the three major dietary sources – vegetables, meat, and water – has been legislated, regulated, and monitored due to public health concerns over cancer risk. In contrast, a growing and compelling body of evidence indicates that dietary nitrate, particularly from vegetables, protects against cardiovascular disease and other chronic diseases. This evidence for the protective effect of nitrate is overshadowed by the potential for nitrate to form carcinogenic N-nitrosamines. Scope and approach: The nitrate content, regulations and estimated intake from vegetables, meat …

Distinct Hyperuricemia Trajectories Are Associated With Different Risks Of Incident Diabetes: A Prospective Cohort Study, Jia Liu, Huiying Pan, Yue Liu, Mengying Guan, Xia Li, Shuo Chen, Xingyao Tong, Yanxia Luo, Xiaonan Wang, Xinghua Yang, Xiuhua Guo, Jingbo Zhang, Lixin Tao

Research outputs 2022 to 2026

Background and aim: Conflicting results suggest a link between serum uric acid and diabetes and previous studies ignored the effect of continuous exposure of serum uric acid on diabetes risk. This study aims to characterize hyperuricemia trajectories in middle-aged adults and to examine its potential impact on diabetes risk, considering the role of obesity, dyslipidemia, and hypertension. Methods and results: The cohort included 9192 participants who were free of diabetes before 2013. The hyperuricemia trajectories during 2009–2013 were identified by latent class growth models. Incident diabetes during 2014–2018 was used as the outcome. Modified Poisson regression models were used to …

Effects Of Different Inlet Configurations On The Performance Of Solar Storage Tanks: A Three-Dimensional Unsteady Cfd Simulation, Abdellah Shafieian, Hamid Reza Bahrami, Amin Roostaee, Seyed Sina Feyzi

Research outputs 2022 to 2026

A validated three-dimensional unsteady computational fluid dynamics analysis is performed in this study to investigate the effects of the inlet flow conditions on the thermal performance of solar storage tanks. First, effects of different mass flow rates on the thermal performance of a simple tank (Model 1, single inlet and single outlet) are investigated. The results show that higher mass flow rates deteriorate thermocline and thermal stratification which is desirable. To reduce the unfavorable effects of inlet jet mixing with stagnant fluid, the mass flow rates are divided equally between two inlet ports in the next model (Model 2). The …

Monitoring The Birds And The Bees: Environmental Dna Metabarcoding Of Flowers Detects Plant–Animal Interactions, Joshua P. Newton, Philip W. Bateman, Matthew J. Heydenrych, Joshua H. Kestel, Kingsley W. Dixon, Kit S. Prendergast, Nicole E. White, Paul Nevill

Research outputs 2022 to 2026

Animal pollinators are vital for the reproduction of ~90% of flowering plants. However, many of these pollinating species are experiencing declines globally, making effective pollinator monitoring methods more important than ever before. Pollinators can leave DNA on the flowers they visit, and metabarcoding of these environmental DNA (eDNA) traces provides an opportunity to detect the presence of flower visitors. Our study, collecting flowers from seven plant species with diverse floral morphologies, for eDNA metabarcoding analysis, illustrated the value of this novel survey tool. eDNA metabarcoding using three assays, including one developed in this study to target common bush birds, recorded …

Reduced Peak Bone Mass In Young Adults With Low Motor Competence, Jocelyn Tan, Carrie Anne Ng, Nicolas H. Hart, Timo Rantalainen, Marc Sim, David Scott, Kun Zhu, Beth Hands, Paola Chivers

Research outputs 2022 to 2026

Although suboptimal bone health has been reported in children and adolescents with low motor competence (LMC), it is not known whether such deficits are present at the time of peak bone mass. We examined the impact of LMC on bone mineral density (BMD) in 1043 participants (484 females) from the Raine Cohort Study. Participants had motor competence assessed using the McCarron Assessment of Neuromuscular Development at 10, 14, and 17 years, and a whole-body dual-energy X-ray absorptiometry (DXA) scan at 20 years. Bone loading from physical activity was estimated from the International Physical Activity Questionnaire at the age of 17 …

Targeted Alveo-Pleural Fistula Endobronchial Valve Treatment Using Ventilation Scintigraphy, Calvin Sidhu, Shoba Ratnagobal, Rajesh Thomas, Gary Y.C. Lee, Enya Drudy, Roslyn J. Francis

Research outputs 2022 to 2026

Persistent air-leaks can be difficult to localize in radiology. Bronchoscopic management of air-leaks requires identification of the leak's location to allow suitable targeted treatment. One-way endobronchial valves have become a suitable option for persistent air-leaks. In this report, a combination scintigraphy and one-way endobronchial valve treatment successfully resolved a persistent air-leak.

The Role Of Blockchain Technology In The Transition Toward The Circular Economy: Findings From A Systematic Literature Review, Abderahman Rejeb, Andrea Appolloni, Karim Rejeb, Horst Treiblmaier, Mohammad Iranmanesh, John G. Keogh

Research outputs 2022 to 2026

The circular economy (CE) has emerged as a paradigm to protect the environment and the well-being of future generations. In parallel, Blockchain technology (BC) has emerged as a critical enabler for accelerating the transition toward a CE. In order to understand and summarize prior research on the role of BC in the CE, we conducted a systematic literature review (SLR) of 70 seminal articles published before July 2022. Six main themes emerged: a) CE approaches and practices, b) BC and the integration of the Internet of Things (IoT), c) sustainable supply chain management, d) BC and the CE in the …

A Review On Deep-Learning-Based Cyberbullying Detection, Md Tarek Hasan, Md Al Emran Hossain, Md Saddam Hossain Mukta, Arifa Akter, Mohiuddin Ahmed, Salekul Islam

Research outputs 2022 to 2026

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, …

Klebsiella Aerogenes Adhesion Behaviour During Biofilm Formation On Monazite, Arya Van Alin, Melissa K. Corbett, Homayoun Fathollahzadeh, M. Christian Tjiam, Andrew Putnis, Jacques Eksteen, Anna H. Kaksonen, Elizabeth Watkin

Research outputs 2022 to 2026

The adsorption behaviour of micro-organisms during the initial attachment stage of biofilm formation affects subsequent stages. The available area for attachment and the chemophysical properties of a surface affect microbial attachment performance. This study focused on the initial attachment behaviour of Klebsiella aerogenes on monazite by measuring the ratio of planktonic against sessile subpopulations (P:S ratio), and the potential role of extracellular DNA (eDNA). eDNA production, effects of physicochemical properties of the surface, particle size, total available area for attachment, and the initial inoculation size on the attachment behaviour were tested. K. aerogenes attached to monazite immediately after exposure to …

Low-Complexity Three-Dimensional Aoa-Cross Geometric Center Localization Methods Via Multi-Uav Network, Baihua Shi, Yifan Li, Guilu Wu, Riqing Chen, Shihao Yan, Feng Shu

Research outputs 2022 to 2026

The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. …

Oriented Crossover In Genetic Algorithms For Computer Networks Optimization, Furkan Rabee, Zahir M. Hussain

Research outputs 2022 to 2026

Optimization using genetic algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the genetic algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case where one attitude is good, the other …

The Validity Of Hawkin Dynamics Wireless Dual Force Plates For Measuring Countermovement Jump And Drop Jump Variables †, Andrew J. Badby, Peter D. Mundy, Paul Comfort, Jason P. Lake, John J. Mcmahon

Research outputs 2022 to 2026

Force plate testing is becoming more commonplace in sport due to the advent of commercially available, portable, and affordable force plate systems (i.e., hardware and software). Following the validation of the Hawkin Dynamics Inc. (HD) proprietary software in recent literature, the aim of this study was to determine the concurrent validity of the HD wireless dual force plate hardware for assessing vertical jumps. During a single testing session, the HD force plates were placed directly atop two adjacent Advanced Mechanical Technology Inc. in-ground force plates (the “gold standard”) to simultaneously collect vertical ground reaction forces produced by 20 participants (27 …

Msrl-Net: A Multi-Level Semantic Relation-Enhanced Learning Network For Aspect-Based Sentiment Analysis, Zhenda Hu, Zhaoxia Wang, Yinglin Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of a given text towards several specific aspects. For implementing the ABSA, one way is to convert the original problem into a sentence semantic matching task, using pre-trained language models, such as BERT. However, for such a task, the intra- and inter-semantic relations among input sentence pairs are often not considered. Specifically, the semantic information and guidance of relations revealed in the labels, such as positive, negative and neutral, have not been completely exploited. To address this issue, we introduce a self-supervised sentence pair relation classification task and propose a …

Are You Cloud-Certified? Preparing Computing Undergraduates For Cloud Certification With Experiential Learning, Eng Lieh Ouh, Benjamin Gan

Research Collection School Of Computing and Information Systems

Cloud Computing skills have been increasing in demand. Many software engineers are learning these skills and taking cloud certification examinations to be job competitive. Preparing undergraduates to be cloud-certified remains challenging as cloud computing is a relatively new topic in the computing curriculum, and many of these certifications require working experience. In this paper, we report our experiences designing a course with experiential learning to prepare our computing undergraduates to take the cloud certification. We adopt a university project-based experiential learning framework to engage industry partners who provide project requirements for students to develop cloud solutions and an experiential risk …

Compositional Prompt Tuning With Motion Cues For Open-Vocabulary Video Relation Detection, Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun

Research Collection School Of Computing and Information Systems

Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary prediction trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. In particular, we present Relation Prompt (RePro) for Open-vocabulary Video Visual Relation Detection (Open-VidVRD), where conventional prompt tuning is easily biased to certain subject-object combinations and motion patterns. To this end, RePro addresses the two technical challenges of Open-VidVRD: 1) the prompt tokens should respect the two different semantic roles of subject and object, and 2) the tuning …

Reinforced Adaptation Network For Partial Domain Adaptation, Keyu Wu, Min Wu, Zhenghua Chen, Ruibing Jin, Wei Cui, Zhiguang Cao, Xiaoli Li

Research Collection School Of Computing and Information Systems

Domain adaptation enables generalized learning in new environments by transferring knowledge from label-rich source domains to label-scarce target domains. As a more realistic extension, partial domain adaptation (PDA) relaxes the assumption of fully shared label space, and instead deals with the scenario where the target label space is a subset of the source label space. In this paper, we propose a Reinforced Adaptation Network (RAN) to address the challenging PDA problem. Specifically, a deep reinforcement learning model is proposed to learn source data selection policies. Meanwhile, a domain adaptation model is presented to simultaneously determine rewards and learn domain-invariant feature …

Link Prediction On Latent Heterogeneous Graphs, Trung Kien Nguyen, Zemin Liu, Yuan Fang

Research Collection School Of Computing and Information Systems

On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inaccessible. Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed. In this paper, we study the challenging and unexplored problem of link prediction on an LHG. As existing approaches depend heavily on type-based information, they are suboptimal …

Graphprompt: Unifying Pre-Training And Downstream Tasks For Graph Neural Networks, Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang

Research Collection School Of Computing and Information Systems

Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune"and "pre-train, prompt"paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on …

Widget Detection-Based Testing For Industrial Mobile Games, Xiongfei Wu, Jiaming Ye, Ke Chen, Xiaofei Xie, Ruochen Huang, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

The fast advances in mobile hardware and widespread smartphone usage have fueled the growth of global mobile gaming in the past decade. As a result, the need for quality assurance of mobile gaming has become increasingly pressing. While general-purpose testing methods have been developed for mobile applications, they become struggling when being applied to mobile games due to the unique characteristics of mobile games, such as dynamic loading and stunning visual effects. There comes a growing industrial demand for automated testing techniques with high compatibility (compatible with various resolutions, and platforms) and non-intrusive characteristics (without packaging external modules into the …

Neural Episodic Control With State Abstraction, Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency.Generally, episodic control-based approaches are solutions that leveragehighly-rewarded past experiences to improve sample efficiency of DRL algorithms.However, previous episodic control-based approaches fail to utilize the latentinformation from the historical behaviors (e.g., state transitions, topological similarities,etc.) and lack scalability during DRL training. This work introducesNeural Episodic Control with State Abstraction (NECSA), a simple but effectivestate abstraction-based episodic control containing a more comprehensive episodicmemory, a novel state evaluation, and a multi-step state analysis. We evaluate ourapproach to the MuJoCo and Atari tasks in OpenAI gym domains. The experimentalresults indicate that NECSA achieves higher …

Automating Arduino Programming: From Hardware Setups To Sample Source Code Generation, Imam Nur Bani Yusuf, Diyanah Binte Abdul Jamal, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

An embedded system is a system consisting of software code, controller hardware, and I/O (Input/Output) hardware that performs a specific task. Developing an embedded system presents several challenges. First, the development often involves configuring hardware that requires domain-specific knowledge. Second, the library for the hardware may have API usage patterns that must be followed. To overcome such challenges, we propose a framework called ArduinoProg towards the automatic generation of Arduino applications. ArduinoProg takes a natural language query as input and outputs the configuration and API usage pattern for the hardware described in the query. Motivated by our findings on the …

Multi-Lingual Multi-Partite Product Title Matching, Huan Lin Tay, Wei Jie Tay, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In a globalized marketplace, one could access products or services from almost anywhere. However, resolving which product in one language corresponds to another product in a different language remains an under-explored problem. We explore this from two perspectives. First, given two products of different languages, how to assess their similarity that could signal a potential match. Second, given products from various languages, how to arrive at a multi-partite clustering that respects cardinality constraints efficiently. We describe algorithms for each perspective and integrate them into a promising solution validated on real-world datasets.

Mando-Hgt: Heterogeneous Graph Transformers For Smart Contract Vulnerability Detection, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudendo, Thanh-Nam Doan, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Smart contracts in blockchains have been increasingly used for high-value business applications. It is essential to check smart contracts' reliability before and after deployment. Although various program analysis and deep learning techniques have been proposed to detect vulnerabilities in either Ethereum smart contract source code or bytecode, their detection accuracy and scalability are still limited. This paper presents a novel framework named MANDO-HGT for detecting smart contract vulnerabilities. Given Ethereum smart contracts, either in source code or bytecode form, and vulnerable or clean, MANDO-HGT custom-builds heterogeneous contract graphs (HCGs) to represent control-flow and/or function-call information of the code. It then …

Resale Hdb Price Prediction Considering Covid-19 Through Sentiment Analysis, Srinaath Anbu Durai, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or …

Predicting Adolescent Intervention Non-Responsiveness For Precision Hiv Prevention Using Machine Learning, Bo Wang, Feifan Liu, Lynette Deveaux, Arlene Ash, Ben Gerber, Jeroan Allison, Carly Herbert, Maxwell Poitier, Karen Macdonell, Xiaoming Li Ph.D., Bonita Stanton

Faculty Publications

Interventions to teach protective behaviors may be differentially effective within an adolescent population. Identifying the characteristics of youth who are less likely to respond to an intervention can guide program modifications to improve its effectiveness. Using comprehensive longitudinal data on adolescent risk behaviors, perceptions, sensation-seeking, peer and family influence, and neighborhood risk factors from 2564 grade 10–12 students in The Bahamas, this study employs machine learning approaches (support vector machines, logistic regression, decision tree, and random forest) to identify important predictors of non-responsiveness for precision prevention. We used 80% of the data to train the models and the rest for …

Predictors And Barriers To Vaccination Among Older Syrian Refugees In Lebanon: A Cross-Sectional Analysis Of A Multi-Wave Longitudinal Study, Berthe Abi Zeid, Tanya El Khoury, Hala Ghattas Ph.D., Marwan F. Alawieh, Zeinab Ramadan, Sirine Anouti, Sawsan Abdulrahim, Stephen J. Mccall

Faculty Publications

Background:

Vaccination is important to prevent morbidity and mortality due to COVID-19 among older Syrian refugees. We aimed to elucidate the predictors of COVID-19 vaccine uptake among Syrian refugees aged 50 years or older in Lebanon and to understand their main reasons for not receiving the vaccine.

Methods:

This was a cross-sectional analysis of a five-wave longitudinal study, conducted through telephone interviews between Sept 22, 2020, and March 14, 2022, in Lebanon. For this analysis, data were extracted from wave 3 (Jan 21–April 23, 2021), which included a question on vaccine safety and on whether participants intended to receive the …

The Development Of Sociomathematical Norms In The Transition To Tertiary Exam-Oriented Individualistic Mathematics Education In An East Asian Context, Woong Lim, Hyunkyoung Yoon, Younggon Bae, Oh Nam Kwon

School of Mathematical & Statistical Sciences Faculty Publications

This study investigates social, mathematical, and sociomathematical norms perceived by college students in an engineering mathematics course and examines the students’ sense of mathematics as signals of individual merit. Data sources include a survey and one-on-one interviews with 38 students. The findings help illustrate student perceptions of academic social norms in a large-lecture course represented by the acquisition model of learning in college, detached from communal and collaborative disciplinary practices. Findings provide insights into the local educational context of an East Asian country as a case study when exam-oriented mathematics is institutionalized as normalcy.

Food Insecurity, Snap Participation And Glycemic Control In Low-Income Adults With Predominantly Type 2 Diabetes: A Cross-Sectional Analysis Using Nhanes 2007-2018 Data, Oluwatobi Alawode, Sarah Humble, Cynthia J Herrick

2020-Current year OA Pubs

INTRODUCTION: Diabetes, characterized by elevated blood glucose levels, affects 13% of US adults, 95% of whom have type 2 diabetes (T2D). Social determinants of health (SDoH), such as food insecurity, are integral to glycemic control. The Supplemental Nutrition Assistance Program (SNAP) aims to reduce food insecurity, but it is not clear how this affects glycemic control in T2D. This study investigated the associations between food insecurity and other SDoH and glycemic control and the role of SNAP participation in a national socioeconomically disadvantaged sample.

RESEARCH DESIGN AND METHODS: Adults with likely T2D and income

RESULTS: The study population included 2084 …

Comparative Characteristics Of Integrin Αdβ2 Binding To Native Fibrinogen And Fibrinogen Modified By Dha Oxidation During Inflammation, Ajibola Ilesanmi

Electronic Theses and Dissertations

2-ω-carboxyethylpyrrole (CEP) is a product of docosahexaenoic acid (DHA) oxidation, which forms covalent adducts with different proteins. CEP-modified proteins can interact with macrophage receptor, integrin αDβ2. This study aims to compare αDβ2 binding to its physiological ligand, fibrinogen, and CEP-modified fibrinogen, which is formed during inflammation. We hypothesize that modification of fibrinogen changes its ligand-binding properties to integrin αDβ2 which can affect macrophage migration and retention. Recombinant αD I-domain and αDβ2-transfected HEK293 cells were used for the experiments. Using biolayer interferometry, we found that the affinity of αD I-domain binding to fibrinogen-CEP was higher than fibrinogen and inhibited by the …

Mankind Is Machine: A Monstrous Posthuman Reading Of Philip K. Dick’S Selected Works, Gabriel Davis

Electronic Theses and Dissertations

The works of Philip K. Dick act as an ideal template for readers to explore what it means to be human in a technologically dominated world. Dick’s emphasis on the usage of androids and artificial intelligence as literary monsters allows for a posthuman reading of the traditional literary monster, notably in how their uncanny nature and behavior helps reveal the synthetic tendencies of humanity. In Do Androids Dream of Electric Sheep?, “Imposter,” and “I Hope I Shall Arrive Soon,” each narrative incorporates artificial intelligence and androids acting as others to reveal the machine-like qualities of Dick’s human characters. This …